Fast-converging iterative gradient decent methods for high pattern fidelity inverse mask design | Zendy

Jue-Chin Yu | Zendy; Peichen Yu | Zendy

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Fast-converging iterative gradient decent methods for high pattern fidelity inverse mask design

Author(s) -

Jue-Chin Yu,

Peichen Yu

Publication year - 2010

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.846568

Subject(s) - computer science , convergence (economics) , inverse , gradient descent , enhanced data rates for gsm evolution , weighting , mathematical optimization , iterative method , fidelity , inverse problem , algorithm , artificial neural network , mathematics , artificial intelligence , telecommunications , mathematical analysis , geometry , economics , economic growth , medicine , radiology

Convergence speed and local minimum issue have been the major issues for inverse lithography. In this paper, we propose an inverse algorithm that employs an iterative gradient-descent method to improve convergence and reduce the Edge Placement Error (EPE). The algorithm employs a constrained gradient-based optimization to attain the fast converging speed, while a cross-weighting technique is introduced to overcome the local minimum trapping.

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